Stat Model Predicts Flat Temperatures Through 2050

While climate skeptics have gleefully pointed to the past decade’s lack of temperature rise as proof that global warming is not happening as predicted, climate change activists have claimed that this is just “cherry picking” the data. They point to their complex and error prone general circulation models that, after significant re-factoring, are now predicting a stretch of stable temperatures followed by a resurgent global warming onslaught. In a recent paper, a new type of model, based on a test for structural breaks in surface temperature time series, is used to investigate two common claims about global warming. This statistical model predicts no temperature rise until 2050 but the more interesting prediction is what happens between 2050 and 2100.

David R.B. Stockwell and Anthony Cox, in a paper submitted to the International Journal of Forecasting entitled “Structural break models of climatic regime-shifts: claims and forecasts,” have applied advanced statistical analysis to both Australian temperature and rainfall trends and global temperature records from the Hadley Center’s HadCRU3GL dataset. The technique they used is called the Chow test, invented by economist Gregory Chow in 1963. The Chow test is a statistical test of whether the coefficients in two linear regressions on different data sets are equal. In econometrics, the Chow test is commonly used in time series analysis to test for the presence of a structural break.

A structural break appears when an unexpected shift in a time series occurs. Such sudden jumps in a series of measurements can lead to huge forecasting errors and unreliability of a model in general. Stockwell and Cox are the first researchers I know of to apply this econometric technique to temperature and rainfall data (a description of computing the Chow test statistic is available here). They explain their approach in the paper’s abstract:

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A Chow test for structural breaks in the surface temperature series is used to investigate two common claims about global warming. Quirk (2009) proposed that the increase in Australian temperature from 1910 to the present was largely confined to a regime-shift in the Pacific Decadal Oscillation (PDO) between 1976 and 1979. The test finds a step change in both Australian and global temperature trends in 1978 (HadCRU3GL), and in Australian rainfall in 1982 with flat temperatures before and after. Easterling & Wehner (2009) claimed that singling out the apparent flatness in global temperature since 1997 is ’cherry picking’ to reinforce an arbitrary point of view. On the contrary, we find evidence for a significant change in the temperature series around 1997, corroborated with evidence of a coincident oceanographic regime-shift. We use the trends between these significant change points to generate a forecast of future global temperature under specific assumptions.

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The climatic effects of fluctuations in oceanic regimes are most often studied using singular spectrum analysis (SSA) or variations on principle components analysis (PCA). In other words, by decomposing rainfall and temperature into periodic components. Such approaches can capture short period phenomena like the effects of El Niño , and the potential impact of longer term phenomena such as the Pacific Decadal Oscillation (PDO) on variations in global temperature. These phenomena take place over a period of years or decades. For finding and testing less frequent regime-shifts different techniques are called for. According to the authors: “An F-statistic known as the Chow test (Chow, 1960) based on the reduction in the residual sum of squares through adoption of a structural break, relative to an unbroken simple linear regression, is a straightforward approach to modeling regime-shifts with structural breaks.” All the statistical details aside, the point here is that a sequence of data that contains sudden shifts or jumps is hard to model accurately using standard methods.

The paper investigates two claims made in the climate literature: first, a proposed regime-shift model of Australian temperature with a slightly increasing trend to 1976, rapidly increasing to 1979 (the shift), and slowly increasing since then; and second, a claim of lack of statistical significance regarding the declining temperature since the El Niño event in 1998. Regarding the first, the authors state: “The increase in Australian temperature of around 0.9°C from the start of the readily available records in 1910 is conventionally modeled as a linear trend and, despite the absence of clear evidence, often attributed to increasing concentrations of greenhouse gases (GHGs).” The main reason to apply econometric techniques to climate time series data it that simple linear forecasting can fail if the underlying data exhibit sudden jumps. “That is, while a forecast based on a linear model would indicate steadily changing global temperatures, forecasts based on shifts would reflect the moves to relatively static mean values,” the study states. The choice of underlying model may also impact estimates of the magnitude of climate change, which is one of the major points put forth by this work.

As for the “cherry picking” assertion, the authors claim that the flat global temperatures since 1998 are not an anomaly but are representative of the actual climate trend. That climate trend exhibits two distinct breakpoints, one in 1978 and another in 1997. The proposed new climate model is what is know as a change point model. Such models are characterized by abrupt changes in the mean value of the underlying dynamical system, rather than a smoothly increasing or decreasing trend. Confidence in the 1978 breakpoint is strengthened by the results for global temperatures since 1910. These data indicate the series can be described as gradually increasing to 1978 (0.05 ± 0.015°C per decade), with a steeper trend thereafter (0.15 ± 0.04°C per decade).

The Chow test since 1978 finds another significant breakpoint in 1997, when an increasing trend up to 1997 (0.13 ± 0.02°C per decade) changes to a practically flat trend thereafter (-0.02 ± 0.05°C per decade). Contrary to claims that the 10 year trend since 1998 is arbitrary, structural change methods indicate that 1997 was a statistically defensible beginning of a new, and apparently stable climate regime. Again, according to the authors: “The significance of the dates around 1978 and 1997 to climatic regimeshifts is not in dispute, as they are associated with a range of oceanic, atmospheric and climatic events, whereby thermocline depth anomalies associated with PDO phase shift and ENSO were transmitted globally via ocean currents, winds, Rossby and Kelvin waves .”

Perhaps most interesting is the application of this analysis to the prediction of future climate change, something GCM climate modelers have been attempting for the past 30 years with little success. Figure 3 from the paper illustrates the prediction for temperatures to 2100 following from our structural break model, the assumptions of continuous underlying warming, regime-shift from 1978 to 1997, and no additional major regime-shift. The projections formed by the presumed global warming trend to 1978 and the trend in the current regime predicts constant temperatures for fifty years to around 2050. This is similar to the period of flat temperatures from 1930-80.

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Prediction of global temperature to 2100, by projecting the trends of segments delineated by significant regime-shifts. The flat trend in the temperature of the current climate-regime (cyan) breaks upwards around 2050 on meeting the (presumed) underlying AGW warming (green), and increases slightly to about 0.2°C above present levels by 2100. The 95% CI for the trend uncertainty is dashed. Figure 3 from Stockwell and Cox.

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What is even more encouraging is that, even though temperatures resume their upward climb after 2050, the predicted increase for the rest of the century is only about 0.2o C above present levels. That is around one tenth the increase generally bandied about by the IPCC and its minions, who sometimes predict as much as a 6°C rise by 2100. It must be kept in mind that this extrapolation is based on a number of simplifying assumptions and does not incorporate many of the complexities and natural forcing factors that are incorporated in GCM programs. Can a relatively simple statistical model be more accurate than the climate modelers’ coupled GCM that have been under continuous development for decades?

Mathematical models based on statistics are often the only way to successfully deal with non-linear, often chaotic systems. Scientists often find that physical reality at its most detailed level can defy their computational tools. Consider fluid flow, which can be either laminar or turbulent. Laminar fluid flow is described by the Navier-Stokes equations. For cases of non-viscus flow, the Bernoulli equation can be used to describe the flow. The Navier-Stokes equations are differential equations while the Bernoulli equation is a simpler mathematical relationship which can be derived from the former by way of the Euler Equation.

The Navier-Stokes equations.

In effect, both are ways of dealing with massive numbers of individual molecules in a flowing fluid collectively instead of individually. At the finest physical level, fluid flow is a bunch of molecules interacting with each other, but trying to model physical reality at the level of atomic interaction would be computationally prohibitive. Instead they are dealt with en mass using equations that are basically statistical approximations of how the uncountable number of molecules in flowing fluid behave. Often such mathematical approximations are accurate enough to be useful as scientific and engineering tools.

Fluid flow around a train using the Navier-Stokes equations. Source: Body & Soul.

If the new model’s prediction is true, global temperatures in 2100 will not even approach the tripwire-for-Armageddon 2°C level set by the IPCC as humanity’s point of no return. Can a statistical model be better at predicting future temperatures than complex yet incomplete GCM? With the lack of theoretical understanding, paucity of good historical data, and overwhelming simplifications that have to be made to make climate models run on today’s supercomputers I would have to say that the statistical model comes off pretty well. Give me a well known statistical technique over a fatally flawed climate model any day.